DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 14 August 2026 has been entered.
The following is a non-final office action in response to the application filed 14 August 2026.
Applicant’s amendments to Claims 1, 10, 19 and 23, have been received and are acknowledged. Claims 2, 5 and 12 were previously cancelled.
Claims 1, 3-11, and 13-23 have been examined and are pending.
Response to Arguments
Applicant's arguments filed 14 August 2026 have been fully considered but they are not persuasive.
With regard to the rejections under 35 USC 101, Applicant argues: (1) Claims 1, 34, 6-11 and 13-23 “…do not recite a judicial exception and are integrated into a practical application…” (Applicant’s response 15-16) The recited instant claims recite “ …iterative retraining limitations…are not commercial or legal interactions and cannot practically be performed in the human mind…A person cannot "adjust...model parameters"….These are operations on the internal state of machine learning models that have no analog in human cognition or commercial practice. Accordingly, the retraining limitations do not recite any judicial exception at all. This result is consistent with USPTO Subject Matter Eligibility Example 39…in which the training claim was held not to recite any judicial exception because "[t]he claim does not recite any of the judicial exceptions enumerated in the 2019 PEG" and "the claim does not recite any method of organizing human activity such as a fundamental economic concept or managing interactions between people."….” (Applicant’s response, 16-17). (2) Applicant further analogizes the instant “ technical improvement to machine learning” to Desjardins. Applicant also asserts “… the present claims solve the technical problem of model error: the first and second machine learning models misclassify credits that fail to correspond to an income source and non-repeating gifts, which corrupts the income calculation. Claims 1, 10, and 19, as amended, solve this problem by feeding the identified errors back as training data and specifying how the models are corrected…Like Desjardins, the present claims specify how the models operate-not merely what result they achieve-by identifying which internal values change (weights, coefficients, or offsets) and by what mechanism (an optimization technique), and by specifying when the loop terminates (a training criterion comprising a number of epochs, a training time, or a performance metric). ….reflect the disclosed improvement to the machine learning models themselves, not merely an improvement to the abstract idea of income validation. …The amended claims do not merely invoke machine learning as a tool. Rather, they recite a concrete, multi-step retraining pipeline that operates on the internal state of the models: providing error-correcting data derived from the models' own results, adjusting model parameters comprising weights, coefficients, or offsets by optimizing them using an optimization technique, and repeating the providing and the adjusting until a training criterion is satisfied. This is a change to how the models work, not a use of the models as a tool to carry out a business judgment…..” (Applicant’s response, 18) (3) Applicant further argues that the instant recited claims are analogous to patent eligible claim 1 of Example 42, noting that the instant “… amended claims analogously convert the output of the retrained models into an updated graphical user interface that is dynamically modified and transmitted for display, rather than merely applying an abstract idea on a generic computer…” Additionally the instant claims are also similar to Claim 3 of Example 47 in that “…used to take concrete action: generating an updated income amount and updated confidence score and modifying the graphical user interface to display them…” (Applicant’s response, 19). Applicant also analogizes the instant claims to Example 35, eligible Claims 2 and 3 which "implement the abstract idea with specific meaningful limitations…" asserting that the instant recited claims also “…follow the same pattern: the iterative retraining loop, the specification of model parameters and optimization technique, and the closing limitation that re-runs the retrained models and regenerates the graphical user interface are the "specific meaningful limitations" that move the claims from the generic side of the line to the eligible side…” (Applicant’s response, 19). (4) Applicant additionally argues that the instant claims are “ significantly more than an abstract idea” noting that the withdrawal of the prior art in the previous office action “…are relevant to the factual question of whether the claimed combination is well-understood, routine and conventional…” (Applicant’s response, 20-21).
Examiner respectfully disagrees. As noted in the rejection previously and below, the invention as recited falls into the category of (methods of organizing human activity) [organizing human activity (commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)] and mathematical concepts. Contrary to Applicant’s assertions “ iterative retraining” and “adjust…model parameters” are abstract ideas that fall at least into the category of mathematical concepts; also, in the instant recited claims, the application of these mathematical concepts to business challenges (i.e. income verification) also fall into the category of organizing human activity. Unlike Desjardins, the instant claims as recited do not improve technology. (Specification, [13] graphical user interface are computer technology that allows for user interaction….[30-35] processor…memory; [40-41] system… programs…machine learning models… training supervised or unsupervised [52] user device…include...general purpose computer….) Rather the recited claim limitations at most use known technology (recited at a high level of generality) as a tool to execute an abstract idea (See MPEP 2106.05 (f)) or merely add insignificant extra-solution activity to the judicial exception (See MPEP 2106.05 (g)). As such, the instant recited claims are at most an improvement to the abstract idea. (Applicant’s arguments 1, 2, 4) Further the instant recited claims are not similar to the patent eligible claims of Example 42 because as noted by Applicant the “output of the retrained models” are “updated” onto a “graphical user interface” “ for display,” in other words the claims recite “apply it” (See MPEP 2106.05 (f)) -- more like patent ineligible Claim 2. Similarly unlike patent eligible claim 3 of Example 47 and the Eligible claims 2 and 3 and Example 35, the instant recited claims are directed to an improvement - albeit a specific improvement -to the abstract idea. As such, Applicant’s arguments are not persuasive. (Applicant’s arguments 3)
Examiner previously withdraw the rejections under 35 USC 103 in the Office Action of 5/20/2026.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-4, 6-11, 13-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
When considering subject matter eligibility under 35 U.S.C. 101, (1) it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include fundamental economic practices; certain methods of organizing human activities; an idea itself; and mathematical relationships/formulas. Alice Corporation Pty. Ltd. v. CLS Bank International, et al., 573 U.S. ____ (2014).
The claimed invention is directed to a judicial exception (i.e. an abstract idea) without significantly more. In the instant case, the claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea.
(1) In the instant case, the claims are directed towards a method and the systems of income validation. In the instant case, Claims 19-20 are directed to a process. Claims 1, 3-4, 6-9, 21, 23 and 10-11, 13-18, 22 are directed to a system.
(2a) Prong 1: Income validation is categorized in/akin to the abstract idea subject matter grouping of: (Mathematical concepts and methods of organizing human activity) [organizing human activity (commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations)]. As such, the claims include an abstract idea.
The specific limitations of the invention are (a) identified to encompass the abstract idea include:
1. (Currently Amended) A dynamic income validation … comprising:
…;
a … the dynamic income validation… to:
…, …, an estimated income amount associated with a customer;
…a plurality of transactions comprising text data, wherein the text data is associated with the plurality of transactions;
dynamically determine, using a first …, a repeating source of deposits by:
extracting the text data comprising one or more symbols from the plurality of transactions by performing optical character recognition on the plurality of transactions; and
identifying from among the plurality of transactions a portion of the text data that repeats and corresponds to one or more credits comprising positive transaction values;
dynamically generate, using a second …, an income amount and a confidence score based on the repeating source of deposits and the estimated income amount using the positive transaction values by:
wherein the second …comprises a data classification model that:
parses through the portion of the text data corresponding to the one or more credits to determine whether each credit is a direct deposit or a different type of deposit;
determines whether a transaction source is repeated and whether a transaction amount is repeated, wherein a repeated transaction source or a repeated transaction amount that is also a credit indicates an income source;
identifies whether one or more first credits fail to correspond to an income source;
responsive to identifying that one or more first credits fail to correspond to an income source:
flags, using a flagging mechanism, the one or more first credits for review via a …; and
removes the one or more flagged credits from the income amount;
classifies a first transaction with a positive transaction value that has a first transaction amount that is not repeated or a first transaction source that is not repeated as a gift; and
excludes credits classified as gifts from calculations used to generate the income amount;
dynamically generate a modified … comprising the income amount and the confidence score;
dynamically … the modified … to a second user device for display;
iteratively retrain the first and second … by:
providing additional data to the first and second machine learning models to correct one or more identified errors in results of the first and second machine learning models, the results comprising one or more of the repeating source of deposits, the income amount, the confidence score, and combinations thereof;
adjusting one or more model parameters of the first and second machine learning models based on the additional data, wherein the one or more model parameters comprise weights, coefficients, or offsets, and wherein the adjusting comprises optimizing the one or more model parameters using an optimization technique; and
repeating the providing and the adjusting until a training criterion is satisfied, wherein the training criterion comprises a number of epochs, a training time, or a performance metric; and
generate, using the first and second machine learning models as retrained, an updated income amount and an updated confidence score, and modify the modified graphical user interface to display the updated income amount and the updated confidence score.
10. (Currently Amended) A dynamic income validation …comprising:
…;
… the dynamic income validation … to:
… an estimated income amount associated with a customer;
… a plurality of transactions comprising text data, wherein the text data is associated with the plurality of transactions;
dynamically determine, using a first …, a repeating source of deposits by:
extracting the text data comprising one or more symbols from the plurality of transactions by performing optical character recognition on the plurality of transactions; and
identifying from among the plurality of transactions a portion of the text data that repeats and corresponds to one or more credits comprising positive transaction values;
dynamically generate, using a second …, an income amount and a confidence score based on the repeating source of deposits and the estimated income amount using the positive transaction values:
wherein the second …comprises a data classification model that:
parses through the portion of the text data corresponding to the one or more credits to determine whether each credit is a direct deposit or a different type of deposit;
determines whether a transaction source is repeated and whether a transaction amount is repeated, wherein a repeated transaction source or a repeated transaction amount that is also a credit indicates an income source;
identifies whether one or more first credits fail to correspond to an income source;
responsive to identifying that one or more first credits fail to correspond to an income source:
flags, using a flagging mechanism, the one or more first credits for review via a …; and
removes the one or more flagged credits from the income amount;
classifies a first transaction with a positive transaction value that has a first transaction amount that is not repeated or a first transaction source that is not repeated as a gift; and
excludes credits classified as gifts from calculations used to generate the income amount;
dynamically generate a modified … comprising the income amount and the confidence score;
dynamically … the modified … to a second user device for display;
iteratively retrain the first and second … by:
providing additional data to the first and second machine learning models to correct one or more identified errors in results of the first and second machine learning models, the results comprising one or more of the repeating source of deposits, the income amount, the confidence score, and combinations thereof;
adiusting one or more model parameters of the first and second machine learning models based on the additional data, wherein the one or more model parameters comprise weights, coefficients, or offsets, and wherein the adiusting comprises optimizing the one or more model parameters using an optimization technique; and
repeating the providing and the adiusting until a training criterion is satisfied, wherein the training criterion comprises a number of epochs, a training time, or a performance metric; and
generate, using the first and second machine learning models as retrained, an updated income amount and an updated confidence score, and modify the modified graphical user interface to display the updated income amount and the updated confidence score.
19. (Currently Amended) A … implemented method comprising:
…, …, an estimated income amount associated with a customer;
… a plurality of transactions comprising text data, wherein the text data is associated with the plurality of transactions;
dynamically determining, using a first …, a repeating source of deposits by:
extracting the text data comprising one or more symbols from the plurality of transactions by performing optical character recognition on the plurality of transactions and
identifying from among the plurality of transactions a portion of the text data that repeats and corresponds to one or more credits comprising positive transaction values;
dynamically generating, using a second …, an income amount and a confidence score based on the repeating source of deposits and the estimated income amount using the positive transaction values by:
wherein the second…comprises a data classification model that:
parses through the portion of the text data corresponding to the one or more credits to determine whether each credit is a direct deposit or a different type of deposit;
determines whether a transaction source is repeated and whether a transaction amount is repeated, wherein a repeated transaction source or a repeated transaction amount that is also a credit indicates an income source;
identifies whether one or more first credits fail to correspond to an income source;
responsive to identifying that one or more first credits fail to correspond to an income source:
flags, using a flagging mechanism, the one or more first credits for review …; and
removes the one or more flagged credits from the income amount;
classifies a first transaction with a positive transaction value that has a first transaction amount that is not repeated or a first transaction source that is not repeated as a gift; and
excludes credits classified as gifts from calculations used to generate the income amount;
dynamically generating a modified … comprising the income amount and the confidence score;
dynamically … the modified … to a second user device for display; and
iteratively retrain the first and second … by:
providing additional data to the first and second machine learning models to correct one or more identified errors in results of the first and second machine learning models, the results comprising one or more of the repeating source of deposits, the income amount, the confidence score, or combinations thereof;and
adiusting one or more model parameters of the first and second machine learning models based on the additional data, wherein the one or more model parameters comprise weights, coefficients, or offsets, and wherein the adiusting comprises optimizing the one or more model parameters using an optimization technique; and
repeating the providing and the adiusting until a training criterion is satisfied, wherein the training criterion comprises a number of epochs, a training time, or a performance metric; and
generating, using the first and second machine learning models as retrained, an updated income amount and an updated confidence score, and modifying the modified graphical user interface to display the updated income amount and the updated confidence score.
As stated above, this abstract idea falls into the (b) subject matter grouping of: mathematical concepts and (methods of organizing human activity) .
Prong 2: When considered individually and in combination, the instant claims are do not integrate the exception into a practical application because the steps of or retrieve… determine by … extracting… and identifying…; … generate by…;…parses…; …determines;…identifies; …flags..; removes….; classifies …; ….excludes… generate… iteratively retain… providing… adjusting…repeating the providing and the adjusting… generate…do not apply, rely on, or use the judicial exception in a manner that that imposes a meaningful limitation on the judicial exception (i.e. the abstract idea).
The instant recited claims including additional elements (i.e. storing…receive… receive or retrieve…extracting... transmitting…for display.) do not improve the functioning of the computer or improve another technology or technical field nor do they recite meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment. The limitations merely recite: “apply it” (or an equivalent), merely include instructions to implement an abstract idea on a computer or merely uses generic computing elements to perform well known, routine, and conventional functions or merely uses a computer as a tool to perform an abstract idea or merely add insignificant extra-solution activity to the judicial exception or generally link the use of the judicial exception to a particular technological environment or field of use (See MPEP 2106.05 (d), (f) and (g))
(2b) In the instant case, Claims 19-20 are directed to a process. Claims 1, 3-4, 6-9, 21, 23 and 10-11, 13-18, 22 are directed to a system.
Additionally, the claims (independent and dependent) do not include additional elements that individually or in combination are sufficient to amount to significantly more than the judicial exception of abstract idea (i.e. provide an inventive concept). As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of: system, processors, memory, device, machine learning model(s) , graphical user interface merely uses a computer as a tool to perform an abstract idea or merely add insignificant extra-solution activity to the judicial exception (See MPEP 2106.05 (f) and (g)) (Specification, [13] graphical user interface are computer technology that allows for user interaction….[30-35] processor…memory; [40-41] system… programs…machine learning models… training supervised or unsupervised [52] user device…include...general purpose computer….)
The dependent claims have also been examined and do not correct the deficiencies of the independent claims.
It is noted that claim (3-4, 6-9, 11, 13-18 and 20-23) introduce the additional elements of wherein clauses further defining claim elements (Claims 3 and 13, 4 and 15, 5, 6, 21…); determine… responsible to determining…generate… transmit …(Claim 7); determine... responsive to determining… modify….(Claims 8, 9 and 18); receive… (Claim 11); generate…determine… responsive to determining… generate…transmit… (Claim 14); generate…responsive to generating…(Claim 17 and 20)…analyzing… identifying… determining… identifying… (Claim 21)..These elements are not a practical application of the judicial exception (i.e. the abstract idea) because the limitations merely recite: “apply it” (or an equivalent), merely include instructions to implement an abstract idea on a computer or merely uses generic computing elements to perform well known, routine, and conventional functions or merely uses a computer as a tool to perform an abstract idea or merely add insignificant extra-solution activity to the judicial exception or generally link the use of the judicial exception to a particular technological environment or field of use (See MPEP 2106.05 (f) and (g))
Further these limitations ( system, processors, memory, device, machine learning model(s) , graphical user interface )taken alone or in combination with the abstract do not amount to significantly more than the abstract idea alone because the elements amount to mere use of a computer a as tool to perform an abstract idea or merely add insignificant extra-solution activity to the judicial exception or merely uses generic computing elements to perform well known, routine, and conventional functions.(See MPEP 2106.05 (f) and (g)) (Specification, [13] graphical user interface are computer technology that allows for user interaction….[30-35] processor…memory; [40-41] system… programs…machine learning models… training supervised or unsupervised [52] user device…include...general purpose computer….)
Therefore, claims 1, 3-4, 6-11, and 13-23 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Prior Art
The closest prior art of record: US 2019/0043127 Al, Mahapatra et al. hereinafter referred to Mahapatra discloses a method and system of verifying income using neural networks and search queries. US 10,796,380 Bl, Mossoba et al. hereinafter referred to as Mossoba is merely another method and system employment status detection including income verification features using a transaction log (i.e. a plurality of transactions). US 2023/0008975 A1, Crudele et al. hereinafter referred to as Crudele is another method and system for verifying an identity of a user based on a data mesh in which the data includes deposits from an employer. Even though the prior art of record discloses the general concepts cited above, the prior art of record fails to teach a second machine learning model which differentiates types of deposits, flags credits for review, classifies the transactions and excludes non-repeating gifts from the income calculations. The specific claim language that the prior art of record fails to teach is the combination of:
dynamically determine, using a first machine learning model, a repeating source of deposits by:
extracting the text data comprising one or more symbols from the plurality of transactions by performing optical character recognition on the plurality of transactions; and
identifying from among the plurality of transactions a portion of the text data that repeats and corresponds to one or more credits comprising positive transaction values;
dynamically generate, using a second machine learning model, an income amount and a confidence score based on the repeating source of deposits and the estimated income amount using the positive transaction values,
wherein the second machine learning model comprises a data classification model that:
parses through the portion of the text data corresponding to the one or more credits to determine whether each credit is a direct deposit or a different type of deposit;
determines whether a transaction source is repeated and whether a transaction amount is repeated, wherein a repeated transaction source or a repeated transaction amount that is also a credit indicates an income source;
identifies whether one or more first credits fail to correspond to an income source;
responsive to identifying that one or more first credits fail to correspond to an income source:
flags, using a flagging mechanism, the one or more first credits for review via a graphical user interface; and
removes the one or more flagged credits from the income amount;
classifies a first transaction with a positive transaction value that has a first transaction amount that is not repeated or a first transaction source that is not repeated as a gift; and
excludes credits classified as gifts from calculations used to generate the income amount;
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 8533110 B2 Kremen et al. – Methods and apparatus for verifying employment via online data
US 12034739 B2 – Crudele et al. – Verification Platform – system and method of identity verification based on a data mesh including income data
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/ASHA PUTTAIA H/Primary Examiner, Art Unit 3691
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